{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "0504.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yananma/5_programs_per_day/blob/master/0504.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PegnZLmRSaGv",
        "colab_type": "text"
      },
      "source": [
        "## 3.6 softmax 回归的从零开始实现"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pKpeytAsT1CW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !pip install mxnet d2lzh"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Qgr5v2JVbQh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline \n",
        "import d2lzh as d2l  \n",
        "from mxnet import autograd, nd "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWgt56bOVm8m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 256 \n",
        "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAMjEcL_VvXr",
        "colab_type": "code",
        "outputId": "5e48239c-44e7-4486-8805-ab27a6e7896c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "for X, y in train_iter:\n",
        "    print(X.shape)\n",
        "    print(y.shape)\n",
        "    print(y[:10])\n",
        "    break "
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(256, 1, 28, 28)\n",
            "(256,)\n",
            "\n",
            "[1 4 2 6 1 3 8 7 6 3]\n",
            "<NDArray 10 @cpu_shared(0)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OKbR59JivX8I",
        "colab_type": "text"
      },
      "source": [
        "reinit "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSChzkeAVxwe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_inputs = 784 \n",
        "num_outputs = 10 \n",
        "\n",
        "W = nd.random.normal(scale=0.01, shape=(num_inputs, num_outputs))\n",
        "b = nd.zeros(num_outputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-dg9Q6lV0U_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "W.attach_grad()\n",
        "b.attach_grad()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "prrAbBnrW9HZ",
        "colab_type": "code",
        "outputId": "3b2bbf1e-fff9-4c25-f9f6-3792e8c8cdf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "X = nd.array([[1, 2, 3], [4, 5, 6]])\n",
        "X.sum(axis=0, keepdims=True)"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[5. 7. 9.]]\n",
              "<NDArray 1x3 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QF5oJ2NaXGQD",
        "colab_type": "code",
        "outputId": "8ed7915f-7e97-4a97-ce8d-c870615c878f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "X.sum(axis=1, keepdims=True)"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[[ 6.]\n",
              " [15.]]\n",
              "<NDArray 2x1 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMxoAgkoXdCj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def softmax(X):\n",
        "    X_exp = X.exp()\n",
        "    partition = X_exp.sum(axis=1, keepdims=True)\n",
        "    return X_exp / partition "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3-2G3BGXLlC",
        "colab_type": "code",
        "outputId": "0ef39ca8-4f79-4ca1-a7d8-b17280096b2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "X = nd.random.normal(shape=(2, 5))\n",
        "X_prob = softmax(X)\n",
        "X_prob, X_prob.sum(axis=1)"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(\n",
              " [[0.26479146 0.41922703 0.14376903 0.04618021 0.1260322 ]\n",
              "  [0.08419192 0.1817623  0.24798082 0.28885728 0.19720767]]\n",
              " <NDArray 2x5 @cpu(0)>, \n",
              " [0.99999994 1.        ]\n",
              " <NDArray 2 @cpu(0)>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "152PugVGXram",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def net(X):\n",
        "    return softmax(nd.dot(X.reshape((-1, num_inputs)), W) + b )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FeqnqBQb9TX",
        "colab_type": "code",
        "outputId": "10e22714-015e-4a0b-91dd-78e68a373544",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "y_hat = nd.array([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",
        "y = nd.array([0, 2], dtype='int32')\n",
        "nd.pick(y_hat, y)"
      ],
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\n",
              "[0.1 0.5]\n",
              "<NDArray 2 @cpu(0)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WfmJDsAcjED",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cross_entropy(y_hat, y):\n",
        "    return -nd.pick(y_hat, y).log()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjFeYeElhobe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def accuracy(y_hat, y):\n",
        "    return (y_hat.argmax(axis=1) == y.astype('float32')).mean().asscalar()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rCTd2xsgqtd",
        "colab_type": "code",
        "outputId": "723d8998-05e4-4778-fbab-a7e256133778",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "accuracy(y_hat, y)"
      ],
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhDGVAz8kyJD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate_accuracy(data_iter, net):\n",
        "    acc_sum, n = 0.0, 0 \n",
        "    for X, y in data_iter:\n",
        "        y = y.astype('float32')\n",
        "        acc_sum += (net(X).argmax(axis=1) == y).sum().asscalar()\n",
        "        n += y.size \n",
        "    return acc_sum / n "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpS5lQPUmIFN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time \n",
        "\n",
        "num_epochs, lr = 5, 0.1 \n",
        "\n",
        "train_ls, train_acc, test_acc_plot = [], [], []\n",
        "def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params=None, lr=None, trainer=None):\n",
        "    for epoch in range(num_epochs):\n",
        "        train_l_sum, train_acc_sum, n, start = 0.0, 0.0, 0, time.time()\n",
        "        for X, y in train_iter:\n",
        "            with autograd.record():\n",
        "                y_hat = net(X)\n",
        "                l = loss(y_hat, y).sum()\n",
        "            l.backward()\n",
        "            if trainer is None:\n",
        "                d2l.sgd(params, lr, batch_size)\n",
        "            else:\n",
        "                trainer.step(batch_size)\n",
        "            y = y.astype('float32')\n",
        "            train_l_sum += l.asscalar()\n",
        "            train_acc_sum += (y_hat.argmax(axis=1) == y).sum().asscalar()\n",
        "            n += y.size \n",
        "            train_ls.append((l / y.size).asscalar())\n",
        "            train_acc.append((y_hat.argmax(axis=1) == y).mean().asscalar())\n",
        "        test_acc = evaluate_accuracy(test_iter, net)\n",
        "        test_acc_plot.append(test_acc)\n",
        "        print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f, time %.1f sec' \n",
        "           % (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc, time.time() - start))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LDqV4wxyogQb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "772bdbec-511a-4b35-d309-475ae6d13251"
      },
      "source": [
        "train_ch3(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size, [W, b], lr)"
      ],
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss 0.7881, train acc 0.749, test acc 0.801, time 8.4 sec\n",
            "epoch 2, loss 0.5747, train acc 0.809, test acc 0.822, time 8.3 sec\n",
            "epoch 3, loss 0.5296, train acc 0.824, test acc 0.830, time 8.2 sec\n",
            "epoch 4, loss 0.5051, train acc 0.830, test acc 0.834, time 8.2 sec\n",
            "epoch 5, loss 0.4896, train acc 0.835, test acc 0.839, time 8.1 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jREnwm5mopEA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "acf23d36-2343-41f3-8cf6-a563928e5010"
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "\n",
        "plt.plot(train_ls, label='train loss sum')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5R0/meWvnANsPFr/GO2PVXp9gaozxrk2ccyafM7bad25BoTe14wn0DkfwGv/f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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOqjvVy_uXHw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "e033df51-32de-4f9a-d589-75d15d4107f4"
      },
      "source": [
        "plt.plot(train_acc, label='train acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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azmfHijED0a9tfc+yzHLYes35xvMyPEFYb7CtX4ZxV3xFR4ntvLW+L2Kioyx7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\nNcUAT0RUTf0/VCKv5Wry06YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npScqrvx2Uae",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "546e6840-bb94-4619-ec22-0c6b6cd359f1"
      },
      "source": [
        "plt.plot(test_acc_plot, label='test acc')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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iX0qtpSDMLAl4DBgN5AOLzWyuu68Na3YPobmCnzCzfsA8oDtQCtwLDAj+arrO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\nUgIQEUlQSgAiIgnq/wNYY0ahqSJW2QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vP7FQPtBjVJ",
        "colab_type": "text"
      },
      "source": [
        "## 3.7 softmax 回归的简洁实现"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ywym6abBqxl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline  \n",
        "import d2lzh as d2l \n",
        "from mxnet import gluon, init \n",
        "from mxnet.gluon import loss as gloss, nn "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61Ol5y8yB4nF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 256 \n",
        "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4q7ISA_ACEXj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "net = nn.Sequential()\n",
        "net.add(nn.Dense(10))\n",
        "net.initialize(init.Normal(sigma=0.01))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfGe0POXCYmK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss = gloss.SoftmaxCrossEntropyLoss()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FheZEm1WCh0u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "7e22b3d3-4237-4e02-b4e4-20dc98a11e81"
      },
      "source": [
        "net.collect_params()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sequential0_ (\n",
              "  Parameter dense0_weight (shape=(10, 0), dtype=float32)\n",
              "  Parameter dense0_bias (shape=(10,), dtype=float32)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03b7uCjNClTU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3Wfi0hvC4YH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "outputId": "30f3f9cc-5b46-4327-bdc1-e93871e90acd"
      },
      "source": [
        "num_epochs = 5 \n",
        "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, trainer)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "epoch 1, loss 0.7888, train acc 0.746, test acc 0.805\n",
            "epoch 2, loss 0.5745, train acc 0.811, test acc 0.824\n",
            "epoch 3, loss 0.5287, train acc 0.824, test acc 0.834\n",
            "epoch 4, loss 0.5045, train acc 0.830, test acc 0.832\n",
            "epoch 5, loss 0.4892, train acc 0.834, test acc 0.840\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PggxYPdYDDdn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "628cf9cc-83e0-44c3-a46a-80444ce78312"
      },
      "source": [
        "net"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequential(\n",
              "  (0): Dense(784 -> 10, linear)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rjUfW0CDSei",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "e8a23de6-2fc8-4da0-b7b7-4111b10926a0"
      },
      "source": [
        "net.collect_params()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sequential0_ (\n",
              "  Parameter dense0_weight (shape=(10, 784), dtype=float32)\n",
              "  Parameter dense0_bias (shape=(10,), dtype=float32)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v32y8TRbDVb0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "2ba2589a-f6d7-443c-bd10-5bc050262c38"
      },
      "source": [
        "for X, y in test_iter:\n",
        "    print(X.shape)\n",
        "    print(y.shape)\n",
        "    break \n",
        "\n",
        "true_labels = d2l.get_fashion_mnist_labels(y.asnumpy())\n",
        "pred_labels = d2l.get_fashion_mnist_labels(net(X).argmax(axis=1).asnumpy())\n",
        "titles = [true + '\\n' + pred for true, pred in zip(true_labels, pred_labels)]\n",
        "\n",
        "d2l.show_fashion_mnist(X[:9], titles[:9])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(256, 1, 28, 28)\n",
            "(256,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 9 Axes>"
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